Explore global development with R

In this exercise, you will load a filtered gapminder dataset - with a subset of data on global development from 1952 - 2007 in increments of 5 years - to capture the period between the Second World War and the Global Financial Crisis.

Your task: Explore the data and visualise it in both static and animated ways, providing answers and solutions to 7 questions/tasks within this script.

Get the necessary packages

First, start with installing and activating the relevant packages tidyverse, gganimate, and gapminder if you do not have them already. Pay attention to what warning messages you get when installing gganimate, as your computer might need other packages than gifski and av

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.5.1     ✔ tibble    3.2.1
## ✔ lubridate 1.9.4     ✔ tidyr     1.3.1
## ✔ purrr     1.0.4     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

Look at the data and tackle the tasks

First, see which specific years are actually represented in the dataset and what variables are being recorded for each country. Note that when you run the cell below, Rmarkdown will give you two results - one for each line - that you can flip between.

str(gapminder)
## tibble [1,704 × 6] (S3: tbl_df/tbl/data.frame)
##  $ country  : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ year     : int [1:1704] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
##  $ lifeExp  : num [1:1704] 28.8 30.3 32 34 36.1 ...
##  $ pop      : int [1:1704] 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
##  $ gdpPercap: num [1:1704] 779 821 853 836 740 ...
unique(gapminder$year)
##  [1] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007
head(gapminder)
## # A tibble: 6 × 6
##   country     continent  year lifeExp      pop gdpPercap
##   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
## 1 Afghanistan Asia       1952    28.8  8425333      779.
## 2 Afghanistan Asia       1957    30.3  9240934      821.
## 3 Afghanistan Asia       1962    32.0 10267083      853.
## 4 Afghanistan Asia       1967    34.0 11537966      836.
## 5 Afghanistan Asia       1972    36.1 13079460      740.
## 6 Afghanistan Asia       1977    38.4 14880372      786.

The dataset contains information on each country in the sampled year, its continent, life expectancy, population, and GDP per capita.

#Spørgsmål 1
theme_set(theme_bw()) 

ggplot(data = subset(gapminder, year == 1952), aes(gdpPercap, lifeExp, size = pop)) +
  geom_point() +
 # scale_x_log10() +
  ggtitle("Graf 1952 uden scale_x_log10()")

Let’s plot all the countries in 1952.

theme_set(theme_bw()) 

ggplot(data = subset(gapminder, year == 1952), aes(gdpPercap, lifeExp, size = pop)) +
  geom_point() +
  scale_x_log10() +
  ggtitle("Graf 1952 med scale_x_log10()")

…

We see an interesting spread with an outlier to the right. Explore who it is so you can answer question 2 below!

Next, you can generate a similar plot for 2007 and compare the differences

ggplot(data = subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop)) +
  geom_point() +
  scale_x_log10() +
  ggtitle("Graf 2007")

…

The black bubbles are a bit hard to read, the comparison would be easier with a bit more visual differentiation.

Questions for the static figures:

  1. Answer: why does it make sense to have a log10 scale (scale_x_log10()) on the x axis? (hint: try to comment it out and observe the result) Der er en outlier i dokumentet der betyder at grafen stiger expotentielt

  2. Answer: In Figure 1: Who is the outlier (the richest country in 1952) far right on the x axis?

# Spørgsmål 2
gapminder %>%
  filter(year == 1952) %>%
  arrange(desc(gdpPercap)) %>%
  head(1)
## # A tibble: 1 × 6
##   country continent  year lifeExp    pop gdpPercap
##   <fct>   <fct>     <int>   <dbl>  <int>     <dbl>
## 1 Kuwait  Asia       1952    55.6 160000   108382.
  1. Fix Figures 1 and 2: Differentiate the continents by color, and fix the axis labels and units to be more legible (Hint: the 2.50e+08 is so called “scientific notation”. You want to eliminate it.)
ggplot(subset(gapminder, year == 1952), aes(gdpPercap, lifeExp, size = pop, color = continent)) +
  geom_point(alpha = 0.7) + 
  scale_x_log10(labels = scales::comma) + 
  labs(title = "Global Development in 1952",
       x = "GDP per capita (log scale)",
       y = "Life Expectancy",
       color = "Continent") +
  theme_minimal()

ggplot(subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop, color = continent)) +
  geom_point(alpha = 0.7) +
  scale_x_log10(labels = scales::comma) +
  labs(title = "Global Development in 2007",
       x = "GDP per capita (log scale)",
       y = "Life Expectancy",
       color = "Continent") +
  theme_minimal()

  1. Answer: What are the five richest countries in the world in 2007?
# Spørgsmål 4
gapminder %>%
  filter(year == 2007) %>%
  arrange(desc(gdpPercap)) %>%
  select(country, gdpPercap) %>%
  head(5)
## # A tibble: 5 × 2
##   country       gdpPercap
##   <fct>             <dbl>
## 1 Norway           49357.
## 2 Kuwait           47307.
## 3 Singapore        47143.
## 4 United States    42952.
## 5 Ireland          40676.

Make it move!

The comparison would be easier if we had the two graphs together, animated. We have a lovely tool in R to do this: the gganimate package. Beware that there may be other packages your operating system needs in order to glue interim images into an animation or video. Read the messages when installing the package.

Also, there are two ways of animating the gapminder ggplot.

Option 1: Animate using transition_states()

The first step is to create the object-to-be-animated

anim <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
  geom_point() +
  scale_x_log10() 
anim

…

This plot collates all the points across time. The next step is to split it into years and animate it. This may take some time, depending on the processing power of your computer (and other things you are asking it to do). Beware that the animation might appear in the bottom right ‘Viewer’ pane, not in this rmd preview. You need to knit the document to get the visual inside an html file.

anim + transition_states(year, 
                      transition_length = 1,
                      state_length = 1)

…

Notice how the animation moves jerkily, ‘jumping’ from one year to the next 12 times in total. This is a bit clunky, which is why it’s good we have another option.

Option 2 Animate using transition_time()

This option smooths the transition between different ‘frames’, because it interpolates and adds transitional years where there are gaps in the timeseries data.

anim2 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
  geom_point() +
  scale_x_log10() + 
  transition_time(year)
anim2

The much smoother movement in Option 2 will be much more noticeable if you add a title to the chart, that will page through the years corresponding to each frame.

Now, choose one of the animation options and get it to work. You may need to troubleshoot your installation of gganimate and other packages

Tasks for the animations:

  1. Can you add a title to one or both of the animations above that will change in sync with the animation? (Hint: search labeling for transition_states() and transition_time() functions respectively)
anim <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, color = continent)) +
  geom_point(alpha = 0.7) +
  scale_x_log10(labels = scales::comma) +
  labs(title = "Global Development in {frame_time}",
       x = "GDP per capita (log scale)",
       y = "Life Expectancy",
       color = "Continent") +
  theme_minimal() +
  transition_time(year) +  
  ease_aes('linear')  

animate(anim, renderer = gifski_renderer())

  1. Can you made the axes’ labels and units more readable? Consider expanding the abbreviated labels as well as the scientific notation in the legend and x axis to whole numbers. Also, differentiate the countries from different continents by color
anim <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, color = continent)) +
  geom_point(alpha = 0.7) +
  scale_x_log10(labels = scales::comma) +
  scale_size_continuous(labels = scales::comma) +
  labs(title = "Global Development in {frame_time}",
       x = "GDP per capita (log scale)",
       y = "Life Expectancy",
       color = "Continent") +
  theme_minimal() +
  transition_time(year) +  
  ease_aes('linear')+ 
  theme(
    plot.title = element_text(size=18, face="bold"),
    axis.title.x = element_text(size=16, face="bold"),
    axis.title.y = element_text(size=16, face="bold"),
    axis.text.x = element_text(size=14),
    axis.text.y = element_text(size=14))

animate(anim, renderer = gifski_renderer())

Final Question

  1. Is the world a better place today than it was in the year you were born? Answer this question using the gapminder data. Define better either as more prosperous, more free, more healthy, or suggest another measure that you can get from gapminder. Submit a 250 word answer with an illustration to Brightspace. Include a URL in your Brightspace submission that links to the coded solutions in Github. [Hint: if you wish to have more data than is in the filtered gapminder, you can load either the gapminder_unfiltered dataset or download more historical data at https://www.gapminder.org/data/ ]
birth_year <- 2002

gapminder %>%
  filter(year %in% c(birth_year, 2007)) %>%
  group_by(year) %>%
  summarise(avg_lifeExp = mean(lifeExp, na.rm = TRUE),
            avg_gdpPercap = mean(gdpPercap, na.rm = TRUE))
## # A tibble: 2 × 3
##    year avg_lifeExp avg_gdpPercap
##   <int>       <dbl>         <dbl>
## 1  2002        65.7         9918.
## 2  2007        67.0        11680.
ggplot(gapminder %>% filter(year %in% c(birth_year, 2007)),
       aes(x = factor(year), y = lifeExp, fill = continent)) +
  geom_boxplot() +
  labs(title = "Life Expectancy Over Time",
       x = "Year",
       y = "Life Expectancy",
       fill = "Continent") +
  theme_minimal()

Mellem 2002 og 2007 oplevede verden en generel vækst i både forventet levealder og BNP per capita. Disse tendenser blev drevet af økonomisk ekspansion, medicinske fremskridt og forbedrede levevilkår i mange lande. Forventet levealder steg i de fleste regioner, især i udviklingslande, hvor bedre adgang til sundhedspleje, vaccinationer og forbedret ernæring spillede en central rolle. Afrika oplevede dog en mere moderat stigning på grund af HIV/AIDS-epidemien, der fortsat påvirkede mange lande negativt. I mere udviklede økonomier fortsatte levealderen med at vokse, understøttet af lavere dødelighed fra sygdomme som hjertekarsygdomme og kræft. Samtidig voksede BNP per capita globalt, drevet af økonomisk vækst, stigende handel og teknologiske fremskridt. Kina og Indien oplevede markant vækst i deres BNP per capita takket være industrialisering, eksportboom og øget produktivitet. I Europa og Nordamerika var væksten mere stabil, mens Latinamerika og Østeuropa også nød godt af økonomiske reformer og højere råvarepriser. Sammenhængen mellem BNP per capita og forventet levealder blev tydeligere i denne periode: lande med stigende indkomstniveauer investerede mere i sundhedspleje og infrastruktur, hvilket førte til længere levetid. Selvom ulighed stadig eksisterede, var 2002-2007 præget af generelle forbedringer i menneskers levevilkår verden over.